SDPDmod (version 0.0.7)

SDPDm: Spatial dynamic panel data lag model with fixed effects maximum likelihood estimation.

Description

This function estimates spatial panel model with fixed effects for static or dynamic model. It includes the transformation approach suggested by Yu et al (2008) and Lee and Yu (2010).

Usage

SDPDm(
  formula,
  data,
  W,
  index,
  model = "sar",
  effect = "individual",
  ldet = NULL,
  lndetspec = list(p = NULL, m = NULL, sd = NULL),
  dynamic = FALSE,
  tlaginfo = list(ind = NULL, tl = TRUE, stl = TRUE),
  LYtrans = FALSE,
  incr = NULL,
  rintrv = TRUE,
  demn = FALSE,
  DIRtrans = FALSE
)

Value

An object of class "SDPDm"

coefficients

coefficients estimate of the model parameters (coefficients1 for dynamic model)

rho

spatial coefficient

sige

residuals variance

llik

the value of the log likelihood function

...

Arguments

formula

a symbolic description for the (static) model to be estimated, not including the dynamic component

data

a data.frame

W

spatial weights matrix

index

the indexes (Names of the variables for the spatial and time component. The spatial is first and the time second.)

model

a models to be calculated, c("sar","sdm"), default = "sar"

effect

type of fixed effects, c("none","individual","time","twoways"), default ="individual"

ldet

type of computation of log-determinant, c("full","mc"). Default "full" for smaller problems, "mc" for large problems.

lndetspec

specifications for the calculation of the log-determinant for mcmc calculation. Default list(p=NULL,m=NULL,sd=NULL), if the number of spatial units is >1000 then list(p=30,m=30,sd=12345)

dynamic

logical, if TRUE time lag of the dependent variable is included. Default = FALSE

tlaginfo

specification for the time lag, default = list(ind=NULL,tl=FALSE,stl=FALSE), see details

LYtrans

logical, default FALSE. If the Lee-Yu transformation should be used for bias correction

incr

increment for vector of values for rho

rintrv

logical, default TRUE, calculates eigenvalues of W. If FALSE, the interval for rho is (-1,1)

demn

logical, if Lee-Yu transformation for demeaning of the variables to remove fixed effects is performed (only used in static models). Default FALSE

DIRtrans

logical, if direct transformation of variables should be used. Default, FALSE (only used in dynamic models with "twoways" effects)

Author

Rozeta Simonovska

Details

Based on MatLab functions sar_jihai.m, sar_jihai_time.m and sar_panel_FE.m

In tlaginfo = list(ind = NULL, tl = TRUE, stl = TRUE):

ind i-th column in data which represents the time lag, if not specified then the lag from the dependent variable is created and the panel is reduced from nt to n(t-1)

tl logical, default TRUE. If TRUE \(y_{t-1}\) (the lagged dependent variable in time is included)

stl logical, default TRUE. If TRUE \(Wy_{t-1}\) (the lagged dependent variable in space and time is included)

References

Yu, J., De Jong, R., & Lee, L. F. (2008). Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large. Journal of Econometrics, 146(1), 118-134.

Lee, L. F., & Yu, J. (2010). Estimation of spatial autoregressive panel data models with fixed effects. Journal of Econometrics, 154(2), 165-185.

Lee, L. F., & Yu, J. (2010). A spatial dynamic panel data model with both time and individual fixed effects. Econometric Theory, 564-597.

See Also

vignette("spatial_model", package = "SDPDmod")

Examples

# \donttest{
library("SDPDmod")
data(Produc, package = "plm")
data(usaww, package = "splm")
form1 <- log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp
mod1  <- SDPDm(formula = form1, data = Produc, W = usaww, index = c("state","year"),
               model = "sar", effect = "individual", LYtrans = TRUE)
summary(mod1)
imp1  <- impactsSDPDm(mod1)
summary(imp1)
mod2  <- SDPDm(formula = form1, data = Produc, W = usaww, index = c("state","year"),
               model = "sdm", effect = "twoways", LYtrans = TRUE,
               dynamic = TRUE, tlaginfo=list(ind = NULL, tl = TRUE, stl = TRUE))
summary(mod2)# }